About:
HDDM is a python toolbox for hierarchical Bayesian parameter
estimation of the Drift Diffusion Model (via PyMC).
Drift Diffusion Models are used widely in psychology and cognitive
neuroscience to study decision making.

Changes:

New and improved HDDM model with the following changes:

Priors: by default model will use informative priors
(see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm)
If you want uninformative priors, set informative=False.

Sampling: This model uses slice sampling which leads to faster
convergence while being slower to generate an individual
sample. In our experiments, burnin of 20 is often good enough.

Inter-trial variablity parameters are only estimated at the
group level, not for individual subjects.